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2020 | OriginalPaper | Chapter

Collaborative Recommendation Method Based on Knowledge Graph for Cloud Services

Authors : Weijia Huang, Qianmu Li, Xiaoqian Liu, Shunmei Meng

Published in: Cloud Computing, Smart Grid and Innovative Frontiers in Telecommunications

Publisher: Springer International Publishing

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Abstract

As the number of cloud services and user interest data soars, it’s hard for users to find suitable could services within a short time. A suitable cloud service automatic recommendation system can effectively solve this problem. In this work, we propose KGCF, a novel method to recommend users cloud services that meet their needs. We model user-item and item-item bipartite relations in a knowledge graph, and study property-specific user-item relation features from it, which are fed to a collaborative filtering algorithm for Top-N item recommendation. We evaluate the proposed method in terms of Top-N recommendation on the MovieLens 1M dataset, and prove it outperforms numbers of state-of-the-art recommendation systems. In addition, we prove it has well performance in term of long tail recommendation, which means that more kinds cloud services can be recommended to users instead of only hot items.

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Metadata
Title
Collaborative Recommendation Method Based on Knowledge Graph for Cloud Services
Authors
Weijia Huang
Qianmu Li
Xiaoqian Liu
Shunmei Meng
Copyright Year
2020
Book
Cloud Computing, Smart Grid and Innovative Frontiers in Telecommunications

Print ISBN: 978-3-030-48512-2

Electronic ISBN: 978-3-030-48513-9

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-48513-9_21

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